基于LO-EKF算法的分布驱动电动汽车状态估计的研究  

A Study on the Vehicle State Estimation for a Distributed-drive EV Based on LO-EKF Algorithms

作　　者：林程[1] 王刚[2] 曹万科[1] 周逢军[1] 

机构地区：[1]北京理工大学、电动车辆国家工程实验室,北京100081 [2]山东交通学院汽车工程学院,济南250023

出　　处：《汽车工程》2014年第11期1316-1320,共5页Automotive Engineering

基　　金：国家自然科学基金(51175043,51205022);电动汽车北京市工程研究中心开放基金(NELEV2014)资助

摘　　要：本文中对分布式驱动电动汽车的状态估计进行研究。首先利用龙伯格状态观测器实时观测对车辆的状态估计影响较大的路面坡度,接着,提出了采用扩展卡尔曼滤波算法,以车辆ESP传感器所获取的数据信息作为观测值,对分布式驱动电动汽车的动力学状态变量进行估计。最后进行Carsim和MATLAB联合仿真。结果表明,基于扩展卡尔曼滤波和龙伯格观测器的车辆状态估计算法能较好的估算出车辆的相关动力学状态值,算法可行,收敛速度较快。A study on vehicle state estimation is conducted for a distributed-drive electric vehicle in this paper. Firstly Luenberger observer （LO） is adopted to observe the road slope, which has significant effects on vehi-cle state estimation. Then extended Kalman filter （ EKF） algorithm is used with the data information obtained from ESP sensor as observed value, the dynamics state variables of distributed-drive electric vehicle are estimated. Final-ly a Carsim-Matlab co-simulation is performed. The results show that the proposed vehicle state estimation algorithm based on LO and EKF is feasible and can well estimate the relevant dynamics state variables of vehicle with a rather high convergence speed.

关 键 词：分布式驱动电动汽车 车辆状态估计 坡度观测 扩展卡尔曼滤波器 龙伯格观测器 

分 类 号：U469.72[机械工程—车辆工程]